Ollama Python Library

The Ollama Python library provides the easiest way to integrate Python 3.8+ projects with Ollama.

Prerequisites

• Ollama should be installed and running
• Pull a model to use with the library: ollama pull <model> e.g. ollama pull gemma4
  • See Ollama.com for more information on the models available.

Install

pip install ollama

Usage

from ollama import chat
from ollama import ChatResponse

response: ChatResponse = chat(
  model='gemma4',
  messages=[
    {
      'role': 'user',
      'content': 'Why is the sky blue?',
    },
  ],
)
print(response['message']['content'])
# or access fields directly from the response object
print(response.message.content)

See _types.py for more information on the response types.

Streaming responses

Response streaming can be enabled by setting stream=True.

from ollama import chat

stream = chat(
  model='gemma4',
  messages=[{'role': 'user', 'content': 'Why is the sky blue?'}],
  stream=True,
)

for chunk in stream:
  print(chunk['message']['content'], end='', flush=True)

Cloud Models

Run larger models by offloading to Ollama’s cloud while keeping your local workflow.

• Supported models: deepseek-v3.1:671b-cloud, gpt-oss:20b-cloud, gpt-oss:120b-cloud, kimi-k2:1t-cloud, qwen3-coder:480b-cloud, kimi-k2-thinking See Ollama Models - Cloud for more information

Run via local Ollama

1. Sign in (one-time):

ollama signin

2. Pull a cloud model:

ollama pull gpt-oss:120b-cloud

3. Make a request:

from ollama import Client

client = Client()

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b-cloud', messages=messages, stream=True):
  print(part.message.content, end='', flush=True)

Cloud API (ollama.com)

Access cloud models directly by pointing the client at https://ollama.com.

1. Create an API key from ollama.com , then set:

export OLLAMA_API_KEY=your_api_key

2. (Optional) List models available via the API:

curl https://ollama.com/api/tags

3. Generate a response via the cloud API:

import os
from ollama import Client

client = Client(host='https://ollama.com', headers={'Authorization': 'Bearer ' + os.environ.get('OLLAMA_API_KEY')})

messages = [
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
]

for part in client.chat('gpt-oss:120b', messages=messages, stream=True):
  print(part.message.content, end='', flush=True)

Custom client

A custom client can be created by instantiating Client or AsyncClient from ollama.

All extra keyword arguments are passed into the httpx.Client.

from ollama import Client

client = Client(host='http://localhost:11434', headers={'x-some-header': 'some-value'})
response = client.chat(
  model='gemma4',
  messages=[
    {
      'role': 'user',
      'content': 'Why is the sky blue?',
    },
  ],
)

Async client

The AsyncClient class is used to make asynchronous requests. It can be configured with the same fields as the Client class.

import asyncio
from ollama import AsyncClient


async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  response = await AsyncClient().chat(model='gemma4', messages=[message])


asyncio.run(chat())

Setting stream=True modifies functions to return a Python asynchronous generator:

import asyncio
from ollama import AsyncClient


async def chat():
  message = {'role': 'user', 'content': 'Why is the sky blue?'}
  async for part in await AsyncClient().chat(model='gemma4', messages=[message], stream=True):
    print(part['message']['content'], end='', flush=True)


asyncio.run(chat())

API

The Ollama Python library's API is designed around the Ollama REST API

Chat

ollama.chat(model='gemma4', messages=[{'role': 'user', 'content': 'Why is the sky blue?'}])

Generate

ollama.generate(model='gemma4', prompt='Why is the sky blue?')

List

ollama.list()

Show

ollama.show('gemma4')

Create

ollama.create(model='example', from_='gemma4', system='You are Mario from Super Mario Bros.')

Copy

ollama.copy('gemma4', 'user/gemma4')

Delete

ollama.delete('gemma4')

Pull

ollama.pull('gemma4')

Push

ollama.push('user/gemma4')

Embed

ollama.embed(model='gemma4', input='The sky is blue because of rayleigh scattering')

Embed (batch)

ollama.embed(model='gemma4', input=['The sky is blue because of rayleigh scattering', 'Grass is green because of chlorophyll'])

Ps

ollama.ps()

System One

import ollama

response = ollama.systemone(
  model='nimble',
  state='Our checkout has returned 500 errors since 9am.',
  questions={
    'team': {
      'type': 'choice',
      'instructions': 'Which team should handle this ticket?',
      'criteria': {'billing': 'Payments and refunds', 'technical': 'Software errors'},
    },
  },
)
print(response.answers['team'])

System One uses POST /v1/systemone and requires Ollama v0.35.0 or later with a compatible local model such as nimble. It returns one JSON response; streaming and cloud models are not supported.

state and question instructions accept text, JSON objects, or arrays. Questions are evaluated in their supplied order:

• choice: 2–26 option keys mapped to descriptions; None uses the key as its description.
• noul: probability of true, with optional {"false": "No", "true": "Yes"} descriptions.
• score: 2–26 descriptions ordered lowest to highest; returns a potentially fractional, zero-based score.

Responses contain model, answers, and usage.input_tokens / usage.output_tokens. Confidence measures probability concentration, not calibrated correctness. Token usage comes from the server; output tokens are not necessarily zero. Optional keep_alive accepts seconds or a duration string. Requests use the client's existing host, headers, and HTTP error handling. The server validates its body and model context limits without truncating input.

Available as ollama.systemone, Client.systemone, and await AsyncClient.systemone. Question and answer types, including SystemOneResponse, are exported from ollama. See the combined question example.

Errors

Errors are raised if requests return an error status or if an error is detected while streaming.

model = 'does-not-yet-exist'

try:
  ollama.chat(model)
except ollama.ResponseError as e:
  print('Error:', e.error)
  if e.status_code == 404:
    ollama.pull(model)